The End of $500K Consulting: How AI Is Rewriting Strategy Research
In the spring of 2025, a company that had existed for five months began winning mandates from private equity firms managing a combined $2 trillion in assets. DiligenceSquared was not pitching on the strength of decades-long relationships or a stable of former investment bankers with gray hair and prestige letterheads. It was pitching on price and speed. Where McKinsey charged $500,000 for a due diligence report, DiligenceSquared delivered a comparable analysis for $50,000. The firm raised a $5 million seed round almost immediately, and its client list kept growing.
That single case study contains a structural argument about the future of strategy research that every founder and CXO in India's mid-market needs to understand — not as a threat, but as a once-in-a-generation opportunity.
What Just Broke in the Consulting Model
The traditional consulting engagement is priced on two things: the cost of analyst labor and the value of institutional brand. A McKinsey report commands a premium not simply because McKinsey's analysts are skilled — though many are — but because the McKinsey brand provides decision-making cover. A board can approve a strategic pivot based on a McKinsey recommendation in a way it cannot approve the same recommendation from an internal team. The brand is a risk-transfer mechanism.
AI has attacked both pillars simultaneously. On the labor side, the work that a team of four analysts spent six weeks doing — market sizing, competitive landscaping, regulatory mapping, comparable company analysis — a well-architected AI system can now accomplish in days. McKinsey itself recognized this early enough to build Lilli, its internal AI knowledge management and research platform, and is now exploring productizing it as a subscription service available outside the firm. When the institution most threatened by AI-driven research disruption builds the disruption tool internally and then looks to sell it externally, the directional signal is clear.
On the brand side, Xavier AI is attempting something more ambitious. Founded by Phil Parker, an INSEAD professor with decades of quantitative market research experience, and Joao Filipe, a former McKinsey consultant, the company has described its goal as building "McKinsey in a Box" — an AI system capable of producing the research output of a top-tier consulting engagement without the top-tier consulting price tag. The firm was targeting a $15 million raise, and the market reception suggested the concept resonated. When a former McKinsey insider is building the alternative, the alternative acquires a credibility it would otherwise take years to establish.
The Indian Market Is the Most Underserved in the World
India's consulting market was valued at $8.17 billion in 2025 and is projected to reach $17.01 billion by 2031. That trajectory implies genuine demand for strategic advisory services. But the distribution of that demand is heavily skewed toward the top of the market — large enterprises, multinational subsidiaries, and companies backed by institutional investors who can absorb the cost of a Big 4 engagement.
Consider what the access gap actually looks like: only 15 percent of Indian MSMEs and mid-market companies have ever engaged a consulting firm. The other 85 percent — more than 5,000 companies with revenues below $1 billion — have made significant strategic decisions about market entry, product pricing, competitive positioning, and capital allocation without the benefit of rigorous external research. Not because the research was unnecessary. Because it was inaccessible.
The cost structure of traditional consulting makes this access gap structurally inevitable. Boutique firms operating in India typically charge one-third the rate of Big 4 for comparable work, but even that rate is prohibitive for a ₹100 crore company deciding whether to enter a new state market or launch a product line. When the decision involves six months of execution and ₹5 crore in capital, spending ₹40 lakh on a research firm is defensible in theory. In practice, the founder makes the call on instinct, market gossip, and whatever data the sales team can pull together in a week.
This is not a failure of founders. It is a market structure failure — one that AI is now dismantling.
The 73% Problem with Outcome-Based Pricing
Research across the consulting industry consistently finds that 73 percent of consulting clients would prefer outcome-based pricing over the traditional time-and-materials model. They want to pay for the answer, not the hours. Traditional consulting firms have resisted this model for an obvious reason: when your primary cost is billable analyst hours, you cannot price on outcomes without taking on significant revenue risk.
AI fundamentally changes that equation. When the marginal cost of generating an additional analysis is near zero — the model runs, the report generates, the human reviewer edits and contextualizes — outcome-based pricing becomes viable. More than viable: it becomes the natural pricing model. DiligenceSquared's $50,000 fixed-price due diligence report is outcome-based pricing in practice. You pay for the deliverable, not the team's calendar.
For Indian mid-market companies, this shift has an immediate and concrete implication. The question of whether to commission strategic research is no longer primarily a budget question. It is a question of whether the expected value of better information exceeds the fixed cost of acquiring it. At $50,000 — or the rupee equivalent at Indian market rates — that calculus looks very different than it did at $500,000.
What AI Consulting Actually Delivers (and What It Does Not)
The risk of the DiligenceSquared narrative is that it reads as a simple substitution story: AI replaces consultants, prices fall, everyone wins. The reality is more nuanced and more interesting.
What AI does exceptionally well is information aggregation at scale. Scanning regulatory filings, competitor pricing databases, customer review corpora, patent applications, supply chain data, and public financial statements is exactly the kind of structured-but-voluminous work where AI systems outperform human analysts on both speed and consistency. A market sizing exercise that a senior analyst might complete in ten days — working from secondary sources, cross-referencing databases, triangulating estimates — an AI system can complete in a matter of hours with comparable accuracy and full source auditability.
What AI does not yet do well is the judgment layer: reading between the lines of management commentary to assess strategic intent, understanding the unwritten dynamics of an industry where two competitors are functionally colluding on pricing, sensing when a market that looks attractive on paper has a structural obstacle that does not appear in the data. This is the human layer that firms like DiligenceSquared and Xavier AI are deliberately preserving — AI for the information architecture, humans for the interpretive synthesis.
The hybrid model is not a transitional compromise. It is likely the stable endpoint for the industry at the mid-market price point. Full human-led research at $500,000. Hybrid AI-augmented research at $25,000-$75,000. Fully automated dashboards and monitoring at subscription pricing. Three tiers, serving three fundamentally different buyer segments.
The Competitive Intelligence Window Is Closing
For India's mid-market founders, the strategic opportunity is time-limited in a specific way. The companies that adopt AI-augmented strategy research early will build a competitive intelligence advantage that compounds. They will make better market entry decisions, price more accurately, identify competitive threats sooner, and allocate capital more efficiently. Over three to five years, that accumulating advantage creates the kind of information asymmetry that is very difficult for late movers to close.
The early-mover window is probably 18 to 24 months. After that, AI-augmented research will be a baseline expectation rather than a competitive differentiator — the same way having a functional website was a differentiator in 2000 and a minimum requirement by 2005.
The $500,000 McKinsey report was never the right product for a ₹200 crore Indian company. The question it posed was always too expensive to answer. AI has changed the price of the answer without changing the quality of the question. The question — who are my real competitors, which markets should I enter, how should I price against a stronger rival — remains exactly as valuable as it always was.
Want to see what AI-augmented competitive research looks like for your category? Get a free competitive scan at leanstrat.co/assessment.